## Advances in Computer Simulation: Lectures Held at the Eötvös Summer School at Budapest, Hungary, 16-20 July 1996Computer simulation has become a basic tool in many branches of physics such as statistical physics, particle physics, or materials science. The application of efficient algorithms is at least as important as good hardware in large-scale computation. This volume contains didactic lectures on such techniques based on physical insight. The emphasis is on Monte Carlo methods (introduction, cluster algorithms, reweighting and multihistogram techniques, umbrella sampling), efficient data analysis and optimization methods, but aspects of supercomputing, the solution of stochastic differential equations, and molecular dynamics are also discussed. The book addresses graduate students and researchers in theoretical and computational physics. |

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### Contents

Introduction to Monte Carlo Algorithms | 1 |

Cluster Algorithms | 17 |

Ferenc Niedermayer | 36 |

Copyright | |

5 other sections not shown

### Common terms and phrases

approximation arc i,j augmenting path average block transformations blocking method Boltzmann calculate capacity cluster algorithm complex compute consider correlation function correlation length coupling critical exponents defined denote density detailed balance discretization discuss disordered equilibrium error example exponential ferromagnetic finite flip gauge theory given H(tn heliport improved estimator interactions interface Ising model label lattice Lett linear loop magnetization Marinari mass balance constraints maximum flow problem Metropolis algorithm Monte Carlo methods multicanonical negative cycles neighbor network flow node noise obtained optimal parallel tempering particle partition function path algorithm phase transition Phys plaquette priori probability probability distribution processor Quantum quenched disorder random field random number reduced cost rejection residual network RFIM sample scaling shortest path shortest path problem single-cluster solution solve spin glass square stochastic differential equations temperature thermal update variables vector zero